Using Bayesian Networks for Risk Assessment in Healthcare System

IEEE Software, 2000

Decision makers in all areas of life (including physicians, generals, scientists, bankers and politicians) must often assess and manage risk when there is little or no direct historical data to draw upon, or where relevant data is difficult to identify. The international credit crisis was not predicted by the world's leading financial analysts because they relied on models based on historical statistical data that could not adapt to new circumstances even when those circumstances (in this case the collapse of the mortgage sub-prime market) were foreseeable by experts with more intimate knowledge of the market place. The challenges are similarly acute when the source of the risk is novel: terrorist attacks, ecological disasters, major project failures, and more general failures of novel systems, market-places and business models.

Journal of Applied Statistics, 2013

2014 IEEE 16th International Conference on e-Health Networking, Applications and Services (Healthcom), 2014

Proceedings of the Second International Symposium on Medical Data Analysis, 2001

Due to the uncertain nature of many of the factors that influence on the performance of an emergency medical service, we propose using Bayesian networks to model this kind of systems. We use an algorithm for learning Bayesian networks to build the model, from the point of view of a hospital manager, and apply it to the specific case of a spanish hospital. We also report the results of some preliminary experimentation with the model.

2004

This paper is about helping people who make critical decisions improve the quality of their judgements. We provide a brief introduction to Bayesian Nets (BNs) and use an example in safety assessment. We show how BNs enable decision-makers to combine different types of evidence (including subjective judgements) to provide quantitative, auditable arguments. By using state-ofthe-art BN technology it is now easy for decision-makers to develop solutions that scale up to the most complex types of problems.

2021

Zenodo (CERN European Organization for Nuclear Research), 2023

This paper provides a practical approach to construct and learn a Bayesian network model that will enable an operational risk manager communicate actionable operational risk information for informed decision making by senior managers. Bayesian networks and their application in operational risk management has been widely studied; however, literature and research has predominantly focused on their application in modeling and measuring operational risk for capital calculation purposes. We detail the approach to construct and learn a BN model, from an incident database, using the machine learning capabilities in the R package bnlearn. The modeling and the inference capabilities of the Bayesian Networks can be applied to business-as-usual risk management techniques such as loss analysis, scenario analysis, risk assessment, development of key risk indicators, and risk reporting.

2014

Coping with an ageing population is a major concern for healthcare organisations around the world. The average cost of hospital care is higher than social care for older and terminally ill patients. Moreover, the average cost of social care increases with the age of the patient. Therefore, it is important to make efficient and fair capacity planning which also incorporates patient centred outcomes. Predictive models can provide predictions which their accuracy can be understood and quantified. Predictive modelling can help patients and carers to get the appropriate support services, and allow clinical decision-makers to improve care quality and reduce the cost of inappropriate hospital and Accident and Emergency admissions. The aim of this study is to provide a review of modelling techniques and frameworks for predictive risk modelling of patients in hospital, based on routinely collected data such as the Hospital Episode Statistics database. A number of sub-problems can be considered such as Length-of-Stay and End-of-Life predictive modelling. The methodologies in the literature are mainly focused on addressing the problems using regression methods and Markov models, and the majority lack generalisability. In some cases, the robustness, accuracy and re-usability of predictive risk models have been shown to be improved using Machine Learning methods. Dynamic Bayesian Network techniques can represent complex correlations models and include small probabilities into the solution. The main focus of this study is to provide a review of major time-varying Dynamic Bayesian Network techniques with applications in healthcare predictive risk modelling.

Reliability Engineering & System Safety, 2015

The use of expert systems can be helpful to improve the transparency and repeatability of assessments in areas of risk analysis with limited data available. In this field, Human Reliability Analysis (HRA) is no exception, and, in particular, dependence analysis is an HRA task strongly based on analyst judgement. The analysis of dependence among Human Failure Events refers to the assessment of the effect of an earlier human failure on the probability of the subsequent ones. This paper analyses and compares two expert systems, based on Bayesian Belief Networks and Fuzzy Logic (a Fuzzy Expert System, FES), respectively. The comparison shows that a BBN approach should be preferred in all the cases characterized by quantifiable uncertainty in the input (i.e. when probability distributions can be assigned to describe the input parameters uncertainty), since it provides a satisfactory representation of the uncertainty and its output is directly interpretable for use within PSA. On the other hand, in cases characterized by very limited knowledge, an analyst may feel constrained by the probabilistic framework, which requires assigning probability distributions for describing uncertainty. In these cases, the FES seems to lead to a more transparent representation of the input and output uncertainty.

Bayesian networks are powerful in data mining and analyzing causal relationships of an uncertain-reasoning problem. The implementation of Baye-sian networks in industry and healthcare diagnosis can facilitate the process of locating causations in complex issues. This study conducted two case studies by BayesiaLab in consumer service and healthcare domain. Case Study One used unsupervised learning and supervised learning on the individual data set of county road traffic volume in Indiana State and concluded that road type has the most significant impact on daily vehicle miles traveled. In Case Study Two, only supervised learning was used to observe the aggregated data of adverse mental health effect on civilians, deployed veterans and nondeployed veterans of different genders. Both types of veterans showed higher probability to have adverse mental health compared to civilians. In conclusion, Bayesian networks provided valid results to support prior research. Further research is needed to investigate the differences between using individual data and aggregated data, and to apply Bayesian networks in meta-analysis.